It is important to determine the location of a mobile device capable of radio communication especially in an emergency situation. One method of assessing geolocation of a mobile device is utilizing the mobile device in conjunction with a geolocation system. Exemplary geolocation systems include satellite navigation systems. For example, one Global Navigation Satellite System (GNSS) is the NAVSTAR Global Positioning System (GPS). GPS is a radio positioning system providing subscribers with highly accurate position, velocity, and time (PVT) information.
FIG. 1 is a schematic representation of a constellation 100 of GPS satellites 101. With reference to FIG. 1, GPS includes a constellation of GPS satellites 101 in non-geosynchronous orbits around the earth. The GPS satellites 101 travel in six orbital planes 102 with four of the GPS satellites 101 in each plane. Of course, a multitude of on-orbit spare satellites may also exist. Each orbital plane has an inclination of 55 degrees relative to the equator. In addition, each orbital plane has an altitude of approximately 20,200 km (10,900 miles). The time required to travel the entire orbit is just under 12 hours. Thus, at any given location on the surface of the earth with clear view of the sky, at least five GPS satellites are visible at any given time.
With GPS, signals from the satellites arrive at a GPS receiver and are utilized to determine the position of the receiver. GPS position determination is made based on the time of arrival (TOA) of various satellite signals. Each of the orbiting GPS satellites 101 broadcasts spread spectrum microwave signals encoded with satellite ephemeris information and other information that allows a position to be calculated by the receiver. Presently, two types of GPS measurements corresponding to each correlator channel with a locked GPS satellite signal are available for GPS receivers. The two carrier signals, L1 and L2, possess frequencies of 1.5754 GHz and 1.2276 GHz, or wavelengths of 0.1903 m and 0.2442 m, respectively. The L1 frequency carries the navigation data as well as the standard positioning code, while the L2 frequency carries the P code and is used for precision positioning code for military applications. The signals are modulated using bi-phase shift keying techniques. The signals are broadcast at precisely known times and at precisely known intervals and each signal is encoded with its precise transmission time.
GPS receivers measure and analyze signals from the satellites, and estimate the corresponding coordinates of the receiver position, as well as the instantaneous receiver clock bias. GPS receivers may also measure the velocity of the receiver. The quality of these estimates depends upon the number and the geometry of satellites in view, measurement error and residual biases. Residual biases include satellite ephemeris bias, satellite and receiver clock errors and ionospheric and tropospheric delays. If receiver clocks were perfectly synchronized with the satellite clocks, only three range measurements would be needed to allow a user to compute a three-dimensional position. This process is known as multilateration. However, given the expense of providing a receiver clock whose time is exactly synchronized, conventional systems account for the amount by which the receiver clock time differs from the satellite clock time when computing a user's position. This clock bias is determined by computing a measurement from a fourth satellite using a processor in the receiver that correlates the ranges measured from each satellite. This process requires four or more satellites from which four or more measurements can be obtained to estimate four unknowns x, y, z, b. The unknowns are latitude, longitude, altitude, and receiver clock offset. The amount b, by which the processor has added or subtracted time, is the instantaneous bias between the receiver clock and the satellite clock.
However, the signal received from each of the visible satellites does not necessarily result in an accurate position estimation. The quality of a position estimate largely depends upon two factors: satellite geometry, particularly, the number of satellites in view and their spatial distribution relative to the user, and the quality of the measurements obtained from satellite signals. For example, the larger the number of satellites in view and the greater the distances between them, the better the geometry of the satellite constellation. Further, the quality of measurements may be affected by errors in the predicted ephemeris of the satellites, instabilities in the satellite and receiver clocks, ionospheric and tropospheric propagation delays, multipath, receiver noise and RF interference. With standalone GPS navigation or geographic location in a communication system adaptable to utilize GPS data, where a user with a GPS receiver obtains code-phase ranges with respect to a plurality of satellites in view, without consulting with any reference station, the user is very limited in ways to reduce the errors or noises in the ranges.
In addition to using GPS signals to determine a position estimate, a mobile location device may have several other sources of information that can work independently or in association with the GPS information to determine a position estimate. For example, the location device may be capable of calculating the Round Trip Time (RTT) from the device to some known location. Additionally, a network element, e.g., such as a base station, may compute the RTT and convey this information to the device. Network receivers with known locations may compute the Time Difference of Arrival (TDOA) of a signal transmitted from the device; this TDOA information may then be conveyed back to the device. A network element may compute an Angle of Arrival (AOA) with respect to the transmissions of the device and convey this information to the location device as well. The device may also be capable of receiving other signals present in the environment from transmitters with known positions such as TV transmissions, cellular transmissions etc., from which other measurements dependent on the location of the device can be obtained. The location device may make measurements on the various transmission of network elements and these Network Measurement Reports (NMRs) for that device can also be used, in association with a calibration database, to estimate the position of the device. A variety of sources may thus be combined at the device in computing the position estimate. Combined source location estimates are termed hybrid locations for the purpose of this application.
Several methods of GPS navigation and mobile device positioning are widely utilized whereby the current position and travel path of a mobile device may be indicated, measured, displayed, and/or superimposed upon a road map of the region in which the mobile device is currently traveling or located. With such an apparatus and/or associated geolocation system it is essential to determine the current position of a mobile device as accurately as possible under various environmental conditions. One typical prior art method produces an output signal from a gyroscope, indicative of changes in the mobile device course direction (i.e., detected as an amount of rotation of the device about a predetermined axis of the gyroscope) was used, with each such change representing a change in the travel direction of the device in relation to a previously determined absolute travel direction. The direction change information from the gyroscope is used in conjunction with distance information to express a distance that has been traveled by the device relative to some preceding estimated position thereof, i.e., distance information obtained based on an output signal from a device speed sensor, to perform dead reckoning (DR) calculations thereby obtaining the estimated current position and travel direction of the device. When a gyroscope is utilized to measure changes in the travel direction of a mobile device, measurements are generally based upon detecting values of angular velocity of rotation about the aforementioned predetermined axis of the gyroscope. When that axis does not correspond to the axis about which the mobile device actually rotates when performing a turn, then the conversion gain of the gyroscope (which is a proportionality constant, predetermined beforehand as a conversion factor for conversion to angular velocity) may differ from the correct conversion factor thereby resulting in a conversion gain error. Furthermore, as a result of differences between output voltages due to variations in detected angular velocity, drift errors may also arise. These errors may adversely affect the current position and/or direction of travel of the respective mobile device.
Wireless signals are generally subject to distortions due to multipath and noise. These disturbances often have a significant detrimental impact on the accuracy of position estimation. Sources of positional information such as RTT, TA, TDOA, AOA, NMR and GPS information may be corrupted to varying degrees by these natural sources of error.
Because of the sources of inaccuracies described above, methods of mobile device position detection are also utilized in the prior art whereby position measurement data conveyed by radio waves transmitted from a source such as a positional satellite are used to periodically obtain absolute position and travel direction data. This data may be employed to correct the positions and directions derived by dead reckoning based on the on-board sensor outputs as described above. However, with any positioning system, the absolute position and direction estimates obtained may contain substantial amounts of randomly varying error as described above. For this reason, position and travel direction information derived from positional data (whether GPS positional data or another type of positional data, or a combination of both such as in hybrid schemes) are generally subjected to a form of filter processing to reduce the effects of the random errors in the data. The filtered result may then be applied to correct the estimated positions and travel directions derived by dead reckoning. A commonly utilized form of such processing is the Kalman filter.
In the prior art, successive sets of corrected mobile device position and travel direction estimates may be combined to obtain an estimate of the path traveled by the mobile device up to a current position. Periodically, the estimated travel path may be applied in map matching processing, i.e., the path is compared with data expressing a road map of a region in which the device is currently traveling or located, to make use of the fact that the device location is generally constrained to streets or freeways, etc. This may further increase the accuracy of a finally estimated current position of the device. In this prior art method, it becomes possible to accurately obtain and display the route traversed by the mobile device and the device's current position. Under normal conditions, such an apparatus may provide accurate results. However substantial amounts of error may arise in the estimated travel direction and position of the device derived by a prior art apparatus in several scenarios. For example, when the mobile device is currently located in an area in which it is difficult or impossible to receive GPS radio waves, e.g., multistory or underground parking lot, tunnel, dense urban environment, etc., or when the mobile device is inoperative and power is initially applied thereto, then the estimated mobile device location and direction may contain a large amount of error. In such conditions, even if acquisition of GPS or other position measurement data is possible, it may not be possible to apply appropriate map matching processing to the results; therefore, the GPS or other position measurement data cannot be used to accurately correct the error which arises in the estimated travel direction and position.
Accordingly, there is a need in the art for map matching for ground truth correction that would overcome the deficiencies of the prior art. The deficiencies of the prior art may be alleviated through the embodiments and techniques described herein that correct estimated positions of the device utilizing a knowledge of the underlying road vector coordinates and identifying and correcting areas of bad acquired positional data through novel methods and apparatus.
Therefore, an embodiment of the present subject matter provides a method for estimating a location of a device. The method comprises the steps of determining for each of a plurality of locations of the device a set of positional data from signals received from a plurality of satellites and filtering the positional data. The method further comprises the step of comparing the filtered positional data with data from a road network database where the comparing is a function of a distance from at least one point defined by a set of the filtered positional data to a road in the road network database and an angle between a line representing a best fit for plural points defined by corresponding plural sets of the filtered positional data to a line defined by a road in the road network database. Another embodiment of the present subject matter may further comprise the step of filtering the compared data as a function of topographical information. A further embodiment of the present subject matter may comprise the step of correcting the compared data by projecting an estimated location on a predetermined road. Yet another embodiment of the present subject matter may comprise the step of correcting the compared data as a function of topographical information.
An additional embodiment of the present subject matter provides a novel device comprising a receiver for receiving signals from a plurality of satellites and circuitry for determining for each of a plurality of locations of the device a set of positional data from the received signals. The device further comprises a filter for filtering the positional data and circuitry for comparing the filtered positional data with data from a road network database to provide an estimated location of the device. The comparison may be a function of a distance from at least one point defined by a set of the filtered positional data to a road in the road network database and an angle between a line representing a best fit for plural points defined by corresponding plural sets of the filtered positional data to a line defined by a road in the road network database.
A further embodiment of the present subject matter provides a system for estimating the location of a mobile station that receives signals from a plurality of sources. The system comprises a receiver for receiving signals from a plurality of transmission sources and circuitry for determining for each of a plurality of locations of mobile station a set of positional data from the received signals. The system further comprises a filter for filtering the positional data and circuitry for comparing the filtered positional data with data from a road network database to provide as estimated location of the mobile station. The comparison may be a function of a distance from at least one point defined by a set of the filtered positional data to a road in the road network database and an angle between a line representing a best fit for plural points defined by corresponding plural sets of the filtered positional data to a line defined by a road in the road network database.
These embodiments and many other objects and advantages thereof will be readily apparent to one skilled in the art to which the invention pertains from a perusal of the claims, the appended drawings, and the following detailed description of the embodiments.